The role of digital governance on carbon emission performance: evidence from the cities in yangtze river delta, China

Rapid industrialization and ambitious socio-economic targets have presented significant challenges to China’s carbon neutrality process. However, digital transformation offers new opportunities for sustainable development. This research examines the influence of digital governance (Digov) on carbon emission performance (Cep) and explores its underlying mechanisms. The study utilizes data from cities in China’s Yangtze River Delta from 2011 to 2019. The results show that digital governance has significantly improved carbon emission performance, a conclusion that remains robust even after conducting a series of rigorous tests and addressing endogeneity concerns. The main impact mechanisms of digital governance on carbon emission performance include energy intensity reduction, energy consumption scale reduction, industrial structure adjustment, and energy consumption structure optimization.Furthermore, the results indicate that the variation in carbon emission reduction due to digital governance can be attributed to differences in city administrative level, city size, and government capacity. In particular, digital governance plays a pivotal role in facilitating the sustainable transformation of resource-based cities.From the perspective of digital governance, this study provides effective recommendations and valuable insights for achieving low-carbon targets and promoting sustainable development of cities.


Introduction
As China's economic prowess, scientific and technological advancements, and overall national influence have reached new heights, there emerges an imperative to steadfastly embrace the principles of green, low-carbon, and high-quality development, thus paving the way towards a harmonious modern society in sync with the environment. Such an approach becomes indispensable for safeguarding the ecological balance, fostering sustainable progress, and ultimately attaining the pivotal goals of carbon peak and carbon neutrality (Zhang et al 2022b). The Chinese government has always attached great importance to building an ecological civilization. The report to the 20th CPC National Congress calls for planning development in high harmony with man and nature, and promoting green and low-carbon economic growth as the critical link to achieving high-quality development.
With the rapid development of China's economy and society, coupled with accelerated urbanization, the demand for energy consumption has skyrocketed. (Wu et al 2021, Xu et al 2022, resulting in China's annual carbon dioxide emissions exceeding 6 billion tons. In fact, China has become one of the largest carbon emitting countries in the world. China's path to carbon neutrality and related goals are of paramount importance in mitigating global climate challenges. China's carbon emissions have been on a fast-growing trend over the past two decades. As a result, there is immense pressure on China to reduce carbon emissions. In this context, how to Any further distribution of this work must maintain attribution to the author-(s) and the title of the work, journal citation and DOI.
promote economic development while pursuing carbon neutrality is the current need for all sectors of Chinese society to actively explore. The current discussions are focused on achieving high-quality economic development, improving people's well-being and quality of life, and reducing carbon emissions. It is also essential to assess the feasibility of achieving the targets for reducing carbon emissions and identify the methods needed to make it possible. There is currently a debate about whether digitalization is the main driver of new development and how to promote energy savings and reduce emissions while transitioning to a low-carbon economy. In addition, studies have shown that digital transformation and governance are widely used and play an essential role in government governance, economic development, social governance, rural construction, agricultural transformation, environmental governance, and ecological protection , Xu et al 2006, Zhang et al 2017, Wu et al 2021.
Digital governance has permeated every aspect of social life. Digital governance, also known as e-governance, is a concept that emerged after e-commerce and e-government, and is an advanced and widely used model of governance in the digital age. In their previous work, Dunleavy introduced the concept of 'digital era governance' (DEG), which refers to the electronic and transparent management of administrative affairs. They identified nine key components of successful digital governance, including web-based utility processing, centralized government-led IT procurement, electronic service delivery, direct rather than intermediary communication, and the promotion of equal power distribution. (Dunleavy et al 2006). Castro and Lopes (2022) believes that digital governance, in a narrow sense, is the interaction of government, civil society, and economic society and the internal operation of government. As information technology becomes more integrated into our daily lives, tools such as 'big data' and 'cloud computing' are being used in government. This broadens the scope and functionality of government. The goal of digital governance is to integrate services that include a wide range of e-government operations and personalized solutions to meet the needs of citizens. According to the theory of digital governance, the core of digital governance is the integration of services and the broad range of e-government, participatory and demand-driven digital governance (Dunleavy et al 2006, Wang and. According to Milakovich, digital governance refers to the use of information and communication technologies to provide mobile services, networking and multi-channel IT for two-way transactions, which should be considered under a broad umbrella. The transition from e-government to digital governance is also explored, emphasizing the importance of information technology and public participation in the change process. (Milakovich 2011). Although there are varying definitions of digital governance, past research has reached a consensus that it involves governance that utilizes digital technology. Based on previous research, this paper explains that digital governance is when the government utilizes technology to address public matters and assist in policy creation and execution. This process is constantly evolving and interactive.
Meanwhile, the focus of carbon emissions performance research has been on three main areas. One is the evaluation index of carbon emission performance, such as energy intensity (carbon emission per unit of GDP) (Ang 1999), carbon index (carbon emissions per unit of energy consumption) (Mielnik and Goldemberg 1999), carbon emissions intensity (carbon emissions per unit of GDP) (Sun 2006), and total factor Malmquist index (Zhou et al 2008). In particular, there are actual emissions versus allowances and carbon prices from a carbon emissions trading perspective (Zhou et al 2020). Secondly, the influencing factors of carbon emission performance are studied. It is generally agreed that economic development (Zhang et al 2022b), energy intensity (Ang 1999, Sun 2006, government environmental regulations (Castro andLopes 2022, Lin andZhang 2023), and green credit policies (Zhang et al 2014, Zhang et al 2022b are essential factors affecting carbon emissions. Scholars suggest controlling industrial pollution can be achieved by adjusting the industrial structure to limit carbon emissions. Thirdly, carbon emission performance is studied at the geographic area level, such as its dynamic changes (Deng and Zhang 2021, Xu et al 2022, Zhang et al 2022b, regional differences in spatial (Li and Ou 2013) and temporal evolution patterns (Wang et al 2020, Zhang andDeng 2022). The fourth area of research is looking at carbon emissions from a corporate perspective and exploring sustainable development paths.
Two major bodies of literature are intimately connected to this paper. One important aspect concerns the impact of digital technologies on carbon efficiency. Several studies have indicated that the swift advancement of digital technologies and their associated industries can potentially accelerate the rate of electricity consumption. (Salahuddin and Alam 2015), which drives increased carbon emissions. On the other hand, developing digital technologies will improve the quality of the environment by reducing greenhouse gases. For instance, various studies have demonstrated that the increased utilization and widespread adoption of the Internet are strongly associated with long-term reductions in carbon emissions. (Shobande 2021, Wen et al 2021. The growth of the digital economy can play a crucial role in achieving 'carbon neutrality' in cities by reducing carbon emissions (Li and Wang 2022). Second, the impact of government actions on carbon emission performance. First is the government through environmental policiesor laws and regulations (Zhang et al 2014), financial decentralization (Zhang et al 2011), administrative intervention (yinying Wu et al 2021), and other means of ecological governance and control of environmental pollution (Chen and Chen 2018). Alternatively, the government is encouraging a change in the way local governments compete with each other to drive economic growth , which has an impact on carbon emissions.
The academic community has extensively studied the impact of digital technology on carbon emissions, which has provided some theoretical support for the impact of digital governance on carbon emissions. However, there have been very few studies on how digital governance affects the government's ability to manage carbon emissions. Thus, there is still a lack of reliable evidence on the impact of digital governance on carbon emissions. On the one hand, the theoretical discussion of the impact of digital governance on carbon emissions in existing studies needs to be improved. On the other hand, empirical tests of the impact of digital governance on carbon emissions need to be improved. In addition, more research should be conducted to explore and test the relationship between digital governance and urban carbon emissions. Government plays an irreplaceable role in environmental governance. Studying the effect of digital governance on carbon emission performance is important in both theory and practice, and should be approached in a systematic and logical manner. Therefore, this paper aims to establish a system for evaluating urban digital governance by studying the effect of digitalization on carbon emission performance. The panel data of Yangtze River Delta cities in China from 2011 to 2019 were selected, and the effect of digital governance on carbon emissions performance and its mechanisms were investigated. The results show that digital governance has significantly promoted carbon emissions performance, especially by reducing energy intensity and energy consumption scale, and adjusting industrial and energy consumption structures. The heterogeneity analysis shows that the promoting effect of digital governance on carbon emission performance is more pronounced in provincial and sub-provincial cities, resource-based cities, cities with high levels of government efficiency and public services, larger scale cities, and first-, second-and third-tier cities.
The potential innovative measures that this paper may propose are as follows. First, while existing research focuses primarily on examining carbon emissions from the perspectives of society, businesses, and individual citizens, this study seeks to further emphasize the role of government. It aims to develop a government-led, multi-stakeholder participatory digital governance model, which in turn investigates the impact of government digital governance on carbon emissions performance. This approach offers a new perspective for carbon reduction efforts. Second, this paper differs from previous studies by examining the impact of digital governance on carbon emission performance from different perspectives, such as industrial structure, energy consumption, government attention, and technological innovation. Unlike previous studies that focus on one approach, this research provides clear and detailed evidence through a multi-perspective analysis. In addition, the innovative decomposition of the quantitative mechanism provides deeper and clearer evidence for the conclusion, resulting in a more comprehensive and flawless research conclusion. This study is thorough and complete. Third, the results of this paper indicate that digital governance has a significant effect on reducing carbon emissions. Thus, this study provides new perspectives and feasible ways to achieve the dual carbon target, which focuses on the role of government in environmental protection. Future carbon reduction policies can consider the integration of digital technology and the role of government in achieving sustainable social development and environmental protection through digital emission reduction and achieving the goal of carbon neutrality and carbon peaking. In conclusion, this paper provides a scientific basis and theoretical reference for reducing urban carbon emissions and promoting high-quality urban development from the perspective of digital governance through a rigorous empirical study.
The rest of this paper is arranged as follows. Section 2 presents the literature review and research hypotheses. Section 3 presents the methodology and model. Section 4 describes the results and discussion. Section 5 provides a conclusion.

Literature review and research hypotheses
With the widespread use of digital technologies such as the Internet, cloud computing, and big data, 'digitalization' has become commonplace in real-life production. Ritter considers the process of applying digital technology as digitization. The studies presented by Caputo et al focus on different levels of firms, organizations, services, and business models and explore the perspective of changes brought about by digital technologies (Sklyar et al 2019, Caputo et al 2021. Digital governance is a multifaceted process that touches many different areas and brings about ongoing changes in all economic and social aspects. The influence of digital governance on carbon emission performance can be understood by looking at the Environmental Kuznets Curve and Digital Governance Theory. According to the Environmental Kuznets Curve theory, environmental quality is mainly affected by scale, technology, and structural effects. Moreover, the CO 2 content of air is an essential indicator of environmental quality. The growth of digital governance will result in an increase in energy consumption and carbon emission (Castro and Lopes 2022), putting more pressure on environmental management and negatively affecting carbon emission performance.
Technological innovation and structural adjustment can also reduce energy consumption to improve carbon emissions performance. The theory of digital governance emphasizes the impact of digital technology on public sector reform. It emphasizes the importance of information technology and information systems and suggests that their combination with social governance can lead to a new model of governance-digital governance or governance in the digital age. According to this theory, digitization can promote the collaborative and networked governance of multiple issues in government. This can reduce fragmentation and increase the effectiveness of the carbon emissions system, leading to improved environmental governance and government carbon emissions performance.
Current research should focus on examining the connection between digital governance and carbon emission performance. This calls for analysis on the influence of the digital economy and digital finance on carbon emission performance, with a dimensional classification and empirical approach. Deng and Zhang argue that digital financial development affects carbon emission performance through the economic growth effect, industrial structure effect, and technological innovation effect (Deng and Zhang 2021). Similar conclusions were reached by Xu et al 2022 andZhang et al 2017. The other side of the coin is to discuss the impact of digital technologies or levels of carbon emissions. For example, Xiao discuss the non-linear impact of digitalization level on the carbon emission performance of enterprises (Xiao and Wang 2023). Digital technology can revolutionize traditional industries, leading to increased intelligence and sustainability (Chen et al 2022). This transformation can bring higher value and lower energy consumption and carbon emissions.
Although the studies mentioned above are helpful references for this paper, they failed to investigate the correlation between digital governance and carbon emissions. The government plays a vital role in affecting carbon emissions, and digital governance can indirectly impact them. First is energy consumption, including structure, scale, and intensity. Established studies generally agree that improving energy use efficiency is key to advancing carbon neutrality goals (Yu et al 2021, Zhang et al 2022b. Digital technology can assist the government in understanding energy market trends and price fluctuations, allowing for control over the overall energy supply through methods such as cross-subsidies, taxes, pricing, and other means (Höpner 2006). The government can also promote coordination between the supply and consumption sides of energy. By utilizing digital operations in the carbon emission trading market, both market mechanisms and administrative interventions can be adjusted accordingly (Wu et al 2021). This allows for better control over the total amount of energy usage and ultimately, the total amount of carbon emissions. Second, the academic community has been discussing the relationship between industrial structure, carbon emissions, and added value to GDP. They have found that carbon emission intensity optimizes the industrial structure that contributes more to the GDP (Zhang et al 2014), while it is higher when the secondary industry has a larger share (Wang et al 2016). The use of digital technology has transformed the way businesses operate, bringing new life to industrial development. Additionally, government policies aimed at industries have encouraged the improvement of local industrial structures. Third is the strength of environmental regulation. Established studies show that ecological legislation alone does not significantly curb excessive local carbon emissions (Cole et al 2005). Environmental regulations have the power not only to monitor production processes, but also to drive technological advances in business models. This can lead to improved pollution treatment capabilities and reduced pollution emissions during production. Through digital technology and platforms, local governments can monitor pollution sources in real time, intelligently, and throughout the process. Eliminating information asymmetry between the government and enterprises improves the quality of government regulation. Improving the enforcement of environmental regulations can lead to greater transparency and openness in the government's environmental quality. Fourth, green technology innovation. Green technology aims to achieve a harmonious coexistence of man and nature by using new technologies to reduce consumption and pollution and improve ecology. It also promotes the construction of an ecological civilization. It is considered an effective way to reduce pollution and carbon emissions. The process of digitalization can lead to the growth and development of human knowledge, facilitate collaboration in scientific research, and eliminate technological barriers among entities such as enterprises, government agencies, and academic institutions. This helps create a more efficient environment for governance, which in turn accelerates the progress of green technology innovation.
Based on the above, this paper argues that in China's booming digital technology era, digital governance may favor carbon emission performance, energy consumption, industrial structure, environmental regulatory efforts, and green technology innovation (in figure 1). Based on the information presented, this paper can form the following hypothesis.
Hypothesis 1. Digital governance improves carbon emission performance.

Models
As an essential indicator of low-carbon development in cities, carbon emission performance is closely related to achieving carbon emission reduction targets and low-carbon economic growth. It can accurately reflect the input-output relationship of the economy. Drawing on the IPAT model structure (York et al 2003) and explore the direct transmission mechanism of digital governance on carbon emission performance. The benchmark econometric model constructed in this paper is set as follows.
Equation (1) uses the FE regression model, where Cep i,t is the explanatory variable carbon emission performance, which measures used to carbon emission performance of city i in year t. Digov i,t is the core explanatory variable of the paper, which is used to measure the development level of digital governance in city i in year t. The model's control variables (Controls i,t ) are explained above. e i,t is a random error term in the model. In addition, the study also controls the City fixed effect (m i ) and the time-fixed effect (z t ). If digital governance effectively improves carbon emission performance, there will be a significant positive return to a .
1 Figure 1. Research framework and mechanism analysis of Digov on cep.
To further analyze the impact of digital governance on carbon emission performance, we establish an intermediary effect model based on hypothesis 1 and model (1) Where Mechanism i t , is an intermediate mechanism variable and Controls i,t are control variables except the mechanism variables. m i and z t are city-fixed effect and time-fixed effect, respectively. e i,t is the random error.

Variables selection 3.2.1. Explanatory variables
The core explanatory variable in this paper is digital governance (Digov). There needs to be an agreement on the meaning and measurement of digital governance. Digital governance is a government-led, multi-stakeholder participatory governance activity based on 'digital technology + governance.' Governance theory has made a value choice called 'digital governance of government' in response to the increasing demand for government services. This choice involves integrating various aspects, such as digitalizing elements, diversifying subjects, networking structures, and making processes more dynamic . Its assessment must consider social participation, technological development, information management, platform governance, and other element (Dawes 2009). Hu Guangwei created a system to measure e-government services' capability, consisting of three dimensions: service content, service methods, and dynamic capability. He has also published index reports for six consecutive years . Professor Wang Yimin's team assesses digital government in six aspects: digital foundation readiness, digital environment support, digital service maturity, digital collaborative governance, digital citizen participation, and digital technology usage (Wang 2014). However, these studies primarily emphasize the role of digital technologies and processes in governance and do not sufficiently explain digital governance in terms of government digital readiness and citizen participation. Therefore, this paper establishes a comprehensive index evaluation system for digital governance at the city level based on existing studies and taking into account the availability of data. Digital governance infrastructure reflects the material support of the digital government. The development of the digital industry reflects the digital information technology service capacity of the city. Digital life and digital participation reflect the depth and breadth of coverage of digital governance. Government digital focus and propensity reflect the government's importance on digital governance. The specific descriptions are shown in table 1, and digital governance index calculation process is included in appendix.

Explained variables
This explanatory variable is carbon emission performance (cep). As an essential indicator of low-carbon development in cities, carbon emission performance is closely related to the achievement of carbon emission reduction targets and low-carbon economic growth (Wang et al 2020, Deng and. It can accurately reflect the input-output relationship of the economy.The SBM model created by Tone is a practical way to measure carbon emission performance, eco-efficiency, and energy efficiency (Tone 2001). Unlike other models, it takes into account the unwanted output produced during production processes. This makes it a popular choice among researchers and analysts. Compared to traditional data envelope models (DEA), SBM models based on undesired outputs effectively address the problem of redundant or flawed input factors. However, as with traditional DEA models, the SBM model has efficiency values between 0 and 1 for the decision units and cannot identify efficiency differences between effective decision units (DUMs) with an efficiency maximum of 1 (Tone 2004). This paper uses the super-efficient SBM model, considering the studies of Tone (2004) and Wang et al (2020), to measure the CO 2 emission performance of Chinese cities. This approach helps ensure that the efficiency analysis generates more accurate efficiency evaluation values.    Where the objective function value of r* is the efficiency value of the decision unit, q j is the weight vector, x, and y are the input and output variables, and S = (S -, S g , S b ) denotes the slack in inputs, desired outputs, and nondesired outputs. The above models are based on the assumption of constant size.

Other variables
Based on the above theoretical analysis and IPAT model, the industrial structure index, energy consumption structure, government regulation strength, green technology innovation, energy scale, and energy intensity are selected as other factors affecting cep. Considering the effects of GDP growth rate and general government fiscal spending on cep, we introduce these factors into the model as control variables. The specific descriptions are shown in table 2.

Data source
The Yangtze River Delta region is the most economically developed area in China, with advanced and mature industrial clusters. Covering about 2.1% of the national territory, the Yangtze River Delta concentrates about 25% of the country's total economic output and over 25% of industrial value added. The region's development is highly concentrated, resulting in significant carbon emissions and posing challenges in transitioning to a lowcarbon trajectory. However, the Yangtze River Delta has significant advantages in technological innovation that offer potential for carbon reduction strategies. Moreover, the Yangtze River Delta is a leader in digital governance and smart city development in China. Its emphasis on using digital means to expand social governance makes it a benchmark region for governance rules and practices. Because of its representativeness and significance (in figure 2), this paper selects the Yangtze River Delta as the object of study.
Considering data availability, this paper selects panel data from 40 prefecture-level cities from 2011 to 2019 in the Yangtze River Delta as the study sample, and we removed Bozhou city because it was missing the key digital governance related data. The data were mainly obtained from Peking University Digital Research Center, Industrial structure index Instru Instru = 1×the added value of the primary industry accounts for the proportion of GDP +2×the added value of the secondary industry accounts for the proportion of GDP +3 ×the added value of the tertiary industry accounts for the proportion of GDP Energy consumption structure Ecstru The proportion of electricity consumption to total energy consumption.

Government Concern
Govec It means the ratio of words about environmental protection to the total words in the government work report measures government environmental concerns.

Green Technology Innovation
Gitec It is measured by the total number of applications and a total number of green patents granted.

Energy Scale
Ecscale It is measured by the ratio of total energy consumption to the total city population by the end of the year. Energy intensity Eintensity It is measured by the GDP ratio in municipal districts to total energy consumption by the end of the year. The average wage of employees Wage It is measured by the average wage of a worker in a year in the city.

Population size
Popu The population density of local cities measures it. Gross regional product growth rate Pgdp It is measured by the variation rate of GDP.  (2021), where they find that  The robust std. err. is given in ( ), respectively, *** , ** , and * illustrate statistical significance at the 1%, 5%, and 10% levels, and Controls are the control variables, and R 2 delegates R square. government regulation plays a positive role in carbon emissions performance in the digital economy era. These studies provide similar evidence from both the urban and industrial levels. However, these findings differ somewhat from the results of Wang and Hu (2022), who argue that the government's use of digital technologies may lead to excessive regulatory behavior, potentially resulting in variations in carbon emission performance. Furthermore, Pu and Fei (2022) and others have indicated that digital finance contributes to increased carbon emissions in the electricity and transportation sectors, which may not fully consider the government's influence.
As the primary governing entity, the government acknowledges the risks posed by environmental pollution and recognizes the necessity of strengthening environmental protection measures. With the rapid development and widespread application of digital technologies, local governments are taking initiatives to enhance the environmental quality within their jurisdictions and promote the implementation of carbon reduction plans. Table 4 shows the results of robustness tests. First, we replace the measurement of carbon emission performance with different models. Considering that adopting different calculation models may affect the results of carbon emissions performance, this paper replaces the super-efficient EBM model to measure carbon emissions performance as shown in column (1), which indicates that the contribution of digital governance to carbon emissions performance is still significant. Second, this study excludes municipalities directly under the central government due to their uneven economic development and digital governance level. Instead, only prefecture-level cities are selected for regression testing. After removing Shanghai, the regression results in column (2) indicate that carbon emission performance is still promoted by digital governance.

Robustness testing and endogeneity treatment 4.2.1. Robustness testing
Third, the extreme values in 1% are removed by winsor2. The regression results show that the contribution of digital governance to carbon emission performance has passed the significance test at the 5% confidence level, which means that the positive effect of digital governance on carbon emission performance is still significant.
Fourth, the introduction of province fixed effects. The results in columns (4) and (5) show that the regression coefficients of the variables do not undergo mean changes and pass the test.

Endogeneity treatment
This paper aims to reduce the impact of omitted variables in the regression model by controlling enough variables. However, it still faces the problem of endogeneity due to the exclusion of unobservable variables. First, we add more control variables to mitigate the effect of omitted variables. This paper explores the relationship between carbon emissions, GDP growth rate, and government fiscal expenditure. It suggests that a rapidly growing economy may contribute to increased carbon emissions and that controlling government spending can help reduce emissions in certain situations. The paper introduces GDP growth rate (Ggdp) and general government budgetary expenditure (Gexp) as control variables to measure these factors. Columns (1) and (2) of table 5 show that digital governance's effect on carbon emission performance is positive at a 5% significance level after considering the Ggdp and Gexp. The robust std. err. is given in ( ), respectively, *** , ** , and * illustrate statistical significance at the 1%, 5%, and 10% levels, and Controls are the control variables, and R 2 delegates R square.
Second, the issue of endogeneity demands careful consideration in this research. The baseline model is susceptible to endogeneity problems stemming from reverse causality and errors in data measurement. To tackle this concern effectively, the present study employs the instrumental variable approach as a remedy for endogeneity. Prior research commonly utilizes historical data on postal and telecommunication services from 1984 and geographical environment (Zhang et al 2022a) as instrumental variables when investigating variables associated with digitalization. Nevertheless, these instrumental variables may not satisfactorily meet the relevance assumption, potentially exerting a direct impact on carbon emissions levels. Consequently, drawing inspiration from Wu et al (2023), who utilized the quantity of research papers as an instrumental variable for enterprise digital transformation, this paper opts to employ the number of university teachers as the instrumental variable. The involvement of university researchers assumes paramount importance in driving technological innovation and fostering knowledge spillover effects in the realm of digital governance. Table 5 presents the results after considering the endogeneity for the instrumental variable approach in columns (4) and (5). The validity of the instrumental variable is confirmed by the test results, which indicate that Ntc exceeds the 15% threshold for the critical values of the Stock-Yogo weak test and has a significant correlation with Digov at the 1% level. Notably, as shown in column (4) of table 5, even with the inclusion of the instrumental variable (Ntc) to mitigate the endogeneity concern, digital governance retains its positive impact on carbon emission performance.
Third, the severity of the problem due to unobservable variables is tested. This paper draws on Altonji et al (2005) and Wu et al (2023) to test the severity of the problem due to unobservable variables. Specifically where b F is the estimated coefficient that includes all control variables (unconstrained regression coefficient), b B is the estimated coefficient comprising some control variables (constrained regression coefficient). The larger the value the smaller the effect of the omitted variable on the coefficient estimates.The analysis results on omitted unobservable variables are presented in table 6. Column (1) reveals that the coefficient changes are minor and fall within the range of 0.0071. This suggests that the extra control variables have a limited impact on the association between Digov and cep. From column (5), we can see F has a minimum value of 5.5786, much larger than the critical value of 1. One could argue that the impact of unmeasurable factors must be at least 5.5786 times greater than that of measurable variables to alter the current findings. This suggests that excluding unmeasurable factors has only a minor effect on the conclusions drawn in this paper.  7). It examines digital governance to improve carbon emission performance comprehensively in terms of industrial structure, energy consumption structure, government environmental concern, green technology innovation, energy scale, and energy intensity. The results in columns (1)-(6) of Panel A show that digital governance affects carbon emission performance through industrial structures and energy consumption structures. The government aims to promote green and high-quality development while ensuring effective governance. To achieve this, the government will incentivize companies to engage in high-quality technological innovation and implement binding regulations to restrict non-green process technologies. This will prevent companies from harming the environment and consuming resources (Zhou et al 2020). Under the government's guidance, enterprises will also follow the requirements of green development for industrial structure transformation, upgrading, and transformation (Wu et al 2021). The optimization of industrial structure can achieve the optimization of energy structure and the promotion of energy-efficient technologies, which can improve energy efficiency and reduce carbon emissions. Industrial structure upgrading promotes the rational flow of production factors between industries, further optimizes resource allocation efficiency, achieves factor productivity improvement and green transformation of enterprises, and thus curbs carbon emissions (Cole et al 2005, Li and. Through digital technology, the government can better understand energy market trends and price changes, energy consumption, and carbon emissions for transportation, simulate expected energy consumption, adjust market mechanisms and administrative interventions, and promptly regulate urban layout planning. The government has set up a reliable system to assist in crucial green and low-carbon technology areas. This includes reinforcing policies encouraging businesses and investors to participate in basic and advanced research. The objective is to promote the energy consumption infrastructure's decarbonization, cleanliness, and security (Xu et al 2006).
Columns (1)-(6) of Panel B show that digital governance has not improved carbon emission performance through increased government attention to environmental issues and green technology innovation, for the following reasons. First, digital governance does not directly provide resources to promote green technology innovation in firms; rather, the impact seems to come more from improved regulatory policies that provide incentives for firms to shift to low-carbon production (Wang et al 2016, Chen and Chen 2018, Cole et al 2005, Lin and Zhang 2023. Second, enterprises may face significant risks and costs when engaging in green technology innovation Wang 2022, Zhang et al 2022b). Unfortunately, limited financial support from the government hinders the widespread adoption of these technologies. Third, although digital governance enhances government environmental monitoring and attention, the lack of competition among local governments, fiscal decentralization, and performance evaluation has led to inefficient allocation of government resources and policy failures (Zhang et al 2011, Hu and. It should be noted that although digital governance has not statistically positively affected carbon emission performance by promoting green technology innovation and increasing government environmental attention, government attention to environmental issues can exert regulatory influence on enterprises, promote energy transition through data integration, encourage technological advancement, and value digital talent in energy-related industries Chen 2018, Hu and. In fact, green technology innovation is an effective way to fulfill the commitment of 'carbon peak and carbon neutrality' (Shobande 2021, A. Zhang and Deng 2022). Therefore, the government should take full  Panel B. Test of government environment concern and green technology innovation The robust std. err. is given in ( ), respectively, *** , ** , and * illustrate statistical significance at the 1%, 5%, and 10% levels, and Controls are the control variables, and R 2 delegates R square.
advantage of digital governance to promote green technology innovation. Reducing carbon emissions remains a critical goal for China's sustainable development.
In Panel C, columns (1)-(6) demonstrate that digital governance impacts carbon emission performance by influencing energy consumption scale and energy intensity. The energy consumption scale measures the total energy consumed by the urban population at the end of the year. Energy consumption intensity calculates the amount of energy consumed per unit of GDP. The lower the intensity, the greater the energy efficiency. Technological innovation drives economic and social changes in energy productivity toward high-quality development. The national energy governance system is now collaborative, with many participants and decision-makers from various sectors. This positive interaction between government, market players, and social organizations has made energy management more secure, orderly, and scientific, reducing energy consumption (Zhang et al 2022b). Governments can address the energy crunch by implementing policies that support highquality energy development. One approach is to enhance the ability of coal to maintain a steady supply, stabilize its prices, and ensure a stable power supply through coal-power linkages. Alternatively, implementing marketbased methods to regulate energy consumption by compelling high energy-consuming enterprises to decrease their electricity usage from the demand side can help alleviate the strain on the energy supply (Zhang et al 2022b). Moreover, renewable energy development has been accelerated in various regions through the establishment of carbon-neutral demonstration zones and renewable energy integrated application demonstration zones. This has stimulated the growth of strategic new industries and opened new avenues for green and low-carbon development in high-carbon sectors, such as energy and chemicals.

Mediation effect decomposition
The above tests make a qualitative analysis of the relevant mechanisms. In order to further test and quantify the above mechanisms, In this paper, the method of Heckman et al (2013) and Gelbach (2016) is adopted to quantify the mechanism decomposition formula.
Where i represents the city, t represents the year, j is the intermediate mechanism, Mechanism i,t j and indicates the effect of urban i in the year t. Moreover, m i and z t are the city-fixed effect and time-fixed effect, respectively,e i,t is the random error.
Based on the study conducted by Gelbach (2016), we can further split the impact effect.
In equation (9), the effect explained of mechanism j is j d   , j j and the remaining unexplained part is g | |  . j Therefore, the effect weight w j explained by the mechanism j is as follows. The corresponding quantitative results are reported in figure 3. The summation yields that the share of explanation due to the industrial structure is 8.34%, due to energy consumption structure is 12.20%, due to energy consumption scale is 28.17%, and due to energy intensity is 40.40%, explaining a total of 89.11% of the effect. This result indicates that the above four aspects of the mechanism examination have strong credibility and some explanatory power. The robust std. err. is given in ( ), respectively, *** , ** , and * illustrate statistical significance at the 1%, 5%, and 10% levels, and Controls are the control variables, and R 2 delegates R square.

Heterogeneity analysis
There is an apparent heterogeneity in the regional distribution of digital governance development level and carbon emission performance due to the influence of the city administrative level, resource distribution, government governance level, economic development, and national policies (table 8).

City administrative level
Columns (1) and (2) in Panel A show that only provincial capitals and sub-provincial cities have significant carbon reduction effects from digital governance. Provincial capitals and sub-provincial cities have developed their political environment and adopted new policy concepts, such as the scientific concept of development and building a harmonious society. These concepts have influenced the government's governance philosophy, which no longer solely focused on economics. While paying attention to economic development, more attention is being paid to the environment and eco-friendliness. On the other hand, China has been a centralized state since ancient times. Under such a political system, all essential resources or factors of production are distributed from the central to the local level and from the higher cities to the lower one in turn. The level of administration in a city determines the extent of executive power, with provincial capitals and sub-provincial cities having more financial and administrative authority. The government has the authority and resources to allocate funding, implement policies, and promote innovation and economic growth within its jurisdiction. This can positively impact achieving carbon neutrality, reducing carbon emissions, and driving digital transformation in government.

Resource-based and non-resource-based cities
This paper uses the National Sustainable Development Plan for Resource-based Cities (2012-2020) released by the State Council of the People's Republic of China in November 2013 to study the impact of digital governance development on carbon emission intensity. The 40 cities in the sample are divided into 11 resource-based cities and 29 non-resource-based cities to explore the differences in the impact on both types of cities. In Panel A, columns (3) and (4) suggest that regional resource differences impact how digital governance influences carbon emission performance. In resource-based cities, the effect of digital governance on carbon emission performance is not statistically significant, but compared with non-resource-based cities, the regression coefficient of digital governance is 6.7 times larger, and evidently showing a greater effect on promoting carbon emission reduction. Therefore, the development of digital governance benefits sustainable transformation for resource-based cities.

Government governance: government efficiency and public service level
Columns (1) and (4) in Panel B show that the contribution of digital governance to carbon emission performance is significant in cities with high government efficiency and high-quality public services.With the rapid progress of information technology, digital technology is slowly being integrated into social life and public services. Digital governance has become a new way to optimize public services and enhance citizen participation and government efficiency. Digital technology has transformed the service process by eliminating organizational and information barriers. It also helps to efficiently allocate and match public service resources and positively manage the relationship between supply and demand. Digital governance has become a valuable tool to improve public services and government governance for the benefit of society. Using digital and intelligent technology can advance technological innovation in governance by improving the system, optimizing processes, and re-engineering the governance system. This can lead to a dual transformation of the governance system and capacity, resulting in scientific, accurate, convenient, efficient, and intelligent government governance.

City rating
Columns (1) and (4) in Panel C suggest that digital governance for carbon emission performance is significant for mega and megacities, firstand second-tier cities, and not for other classes of cities. The rank of a city is closely related to the number of its inhabitants, the size of city, the level of its GDP, its geographical location, etc The city's level is a crucial factor that affects various aspects of urban life, including infrastructure investment, talent attraction, transportation facilities, entrepreneurial environment, and educational environment. As a result, a significant urban agglomeration effect leads to the concentration of digital governance resources. Moreover, the uneven distribution of resources between cities, such as urban and rural areas, uneven infrastructure development, and income inequality of social groups is very easy to different levels of the digital divide. The digital divide will get worse with the skew of uneven development. The digital divide creates new development gaps in the information society among different groups, posing challenges to social harmony and potentially intensifying the division of society between rich and poor.

Conclusion
Previous literature has extensively examined how digital technology, government policies, and the digital economy affect carbon emissions performance. This paper complements this part of literature by focusing on a relatively less studied effect of carbon emission, namely how digital governance could affect carbon emission performance. We test the hypothesis using panel data to on 40 cities in the Yangtze River Delta region from 2011 to 2019. We also explore the impact mechanisms through mediating mechanism analysis and decomposition.
The empirical results demonstrate that digital governance has positive impacts carbon emission performance.
Our results were further confirmed by rigorous analysis. The tests for mediating mechanism suggest that digital governance may influence carbon emission performance through industrial structure, energy consumption scale and structure, and energy intensity. Our study also found that the impact of digital governance is more specific in certain areas, such as provincial and sub-provincial cities or resource-based locations, cities with higher government efficiency or public services, and high-ranking cities. These findings carry valuable lessons for policymakers. Digital governance creates new opportunities for sustainable development. For governments around the world, the key issue is to maximize the benefits brought about by digitalization and minimize carbon emissions. Traditionally, government aims to design policies that could reduce carbon emissions. The findings in this paper demonstrate that the use of digital governance is beneficial for carbon reduction in air quality to a certain extent. At the policy level, the government should utilize digital governance to provide more precise and clear policies and financial support for enterprises that innovate green technologies. Additionally, efforts should be made to further minimize the scattering and consumption of governance resources. That way, the efficiency of green technology innovation can be enhanced and the positive benefits of digital governance for sustainable development can be fully realized. At the same time, the Government should, on the existing basis, fully utilize digital means to monitor and forecast CO2 emissions and the energy market in order to make timely macroeconomic adjustments. Digital governance can offer environmental benefits and enhance social welfare in China by decreasing carbon emissions, addressing climate change, improving government efficiency, and transforming management. This provides additional support for developing a robust digital network and power. As a result, it is necessary to reinforce the digital infrastructure and expedite the integration of physical government and digital government.
Some improvements can be made in further studies. First, this study selects the China's Yangtze River Delta region, which is the most economically active and under the most pressure to reduce carbon emissions. Nevertheless, this study area is still limited. More samples should be expanded to draw broader conclusions. Second, digital transformation has permeated every aspect of society. This paper explores the model of government-led digital governance. But the interactions between more governance subjects and the effects they bring are not clear from this paper. Future research can provide new evidence from the perspective of multiple governance subjects. Third, digital governance is a new concept, and there has yet to be a systematic measure of this indicator in previous literature. We construct a complete digital governance indicator system based on the existing indicators. However, due to data limitations, it is still not comprehensive enough. Future research could focus on completing the indicator system of digital governance.

Data availability statement
The data cannot be made publicly available upon publication because they are not available in a format that is sufficiently accessible or reusable by other researchers. The data that support the findings of this study are available upon reasonable request from the authors.

Disclosure statement
No potential conflict of interest was reported by the authors.

Author contributions
Methodology, Feiyang-Lin and Xuan-Zhang; software, Xuan-Zhang and Feiyang-Lin; data, Yunhui-Wang, Feiyang-Lin, and Xuan-Zhang; writing-original draft preparation,Yunhui-Wang, Feiyang-Lin, and Xuan-Zhang; writing-review and editing, Yunhui-Wang, Feiyang-Lin, Meijuan-Peng, and Xuan-Zhang; funding acquisition, Yunhui-Wang, Meijuan-Peng, and Xuan-Zhang. Yunhui-Wang, Xuan-Zhang, and Feiyang-Lin contributed equally to this work. All authors have read and agreed to the published version of the manuscript. where E j is the entropy of the information contained in indicator j and n is the number of indicators. Then, the contribution degree of each index to the total system in the subsystem of digital governance is calculated respectively. It is to determine the specific weight of each j indicator. i.e., W j (j = 1, 2, 3, 4, K, n). Where, W j is the specific weight of indicator j, -1 E j is the redundancy of information entropy for calculating indicator j, and m is the number of evaluation years.
Finally, the geometric averaging method and the linear weighting method were further used. The evaluation score of indicator j is calculated, and the comprehensive development index of digital government governance.